The Executive Support System of Ontario, Canada
Bibliographic record
Abstract
The Ministry of Transportation of Ontario (MTO), Canada, is currently implementing an asset management business framework (AMBF). The AMBF provides the ministry with an ambitious blueprint for incorporating asset management concepts into its existing business processes. A key component in the AMBF is the ability to integrate results from the ministry's existing management systems. In support of the AMBF, MTO has developed a prototype executive support system (ESS). The ESS is a what-if analysis tool that predicts network performance over time using data from the ministry's pavement and bridge management systems. It enables decision makers to evaluate the relationship between performance and budget and to view results by region, corridor, or functional class. This paper presents the analytical approach used to develop the ESS and describes how it was implemented by MTO. The ESS uses a candidate-based approach to system integration, which enables the integration of any management system capable of generating work candidates and estimating their impact on a defined set of performance measures. The ESS brings together data from these systems and performs an additional level of cross-asset economic optimizations, taking into account user-defined operating assumptions. Although much work has recently been done on the integration of pavement and bridge systems, the objective of this paper is to present a practical example implemented by MTO.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.231 | 0.073 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".